Introduction


I decided to focus on university students’ behavior on campus, particularly in relation to their study habits and utilisation of resources and activities beyond their regular lectures or labs.

When designing the form, I followed Guideline 2: Only ask for data you actually need. I selected relevant questions, avoiding unnecessary inquiries to prevent data overload. For example, I included questions about frequency of campus visits, study preferences, and resource utilization, directly relating to understanding student behavior. I also ensured questions were logically related, such as not asking about post-university schedules if respondents indicated they didn’t stay on campus after lectures.

The data collected from my form would enable analysis of behavior changes over different weeks. For instance, the question on the frequency of university visits provides quantitative data that can be tracked over time to observe trends in campus visitation. By collecting responses weekly, fluctuations in students’ presence on campus can be analysed over time, potentially identifying patterns related to academic schedules or external factors. Some students may prefer to study alone in one week and with buddies other times so answering questions like “Do you prefer studying alone or with study buddies?” would allow me to find the trend and whether there are specific conditions (ie. exam week, assignments) that make them choose one option over the other.

Here’s the link to the Google Form: University Student Weekly Behavior on Campus

Analytics

learning_data <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSPIBMo6Gm0B8bF6wlpQGMyFMhq1cGMVxRLYyHCsoPsMRNf7Oi3osqPoeQEmdiynyB_Sj3lnWIrgwwS/pub?gid=1646154235&single=true&output=csv")

learning_data_renamed <- learning_data %>%
  rename(timestamp = 1,
         uni_visit_per_week = 2,
         attend_all_lectures = 3,
         uni_study_locations = 4,
         study_preference = 5,
         study_hours_per_day= 6,
         resource_utilise_freq = 7,
         stay_after_uni = 8,
         stay_after_uni_reason = 9)

Static statements

#checkboxes ---- uni_study_locations (removed NA for those that did not answer)

learning_data_renamed_longer <- na.omit(learning_data_renamed) %>%
  separate_rows(uni_study_locations, sep = ", ")
learning_data_renamed_longer  %>%
  ggplot() +
  geom_bar(aes(x = uni_study_locations), fill = "#B1D4E0")+
  labs(title = "Busiest study location at UOA",
       subtitle = "Comparing university study locations most uni students frequents",
       caption = "Source: Survey results",
       x = "UOA study spots",
       y = "Number of UOA students")


Almost half of the UOA students that responded to my survey frequently utilise the Engineering building and Kate Edgar for studying.

Interestingly, it looks like most of these respondents are either not aware of the new arts building or do not prefer studying there.

This is useful information to me as now I have decided to frequent the new art building more often as it is the least busiest!

#attend_all_lectures vs stay_after_uni_reason
ggplot(data = na.omit(learning_data_renamed)) +
  geom_bar(aes(y=attend_all_lectures,fill = stay_after_uni_reason))+
  labs(title = "Socialising over attending lectures?",
       subtitle = "Comparing attending all lectures and staying past uni schedule reasons for uni students",
       caption = "Source: Survey results",
       x = "Number of UOA students",
       y = "Attending all lectures or attend uni when necessary")



Most of the UOA students that responded to my survey only attend UOA when necessary (ie. labs, tutorials, getting help with work, etc.)

Out of those that stay after their uni schedule to socialise, most don’t attend all their lectures. Similarly, out of those that stay after their uni schedule to finsih up labs/ projects/ etc., most don’t attend all their lectures.

This makes me curious whether not attending all lectures leads to finishing up compulsary activities last minute or whether the workload of those compulsory activity leads them to not attend lectures or maybe there is no correlation or cause at all1. Maybe I could conduct an experiment later to find out!

# resource_utilise_freq vs attend_all_lectures
ggplot(data = learning_data_renamed) +
  geom_bar(aes(y = resource_utilise_freq,fill = attend_all_lectures))+
  labs(title = "Who utilises university resources such as libraries or study rooms for studying more?",
       subtitle = "Comparing resource utilisation frequency and attending all lectures for uni students",
       caption = "Source: Survey results",
       x = "Number of UOA students",
       y = "University Resource Utilisation range(1-5)")



The UOA students that responded to my survey and only attended uni when necessary (compulsory activities) utilised the resources more than those than attened all lectures.

I wonder if not attending lectures mean utilising more resources because some of those resources require booking such as study rooms(so they might not be able to leave the booking for lectures)

# stay_after_uni vs study_preference
ggplot(data = learning_data_renamed) +
  geom_bar(aes(y = study_preference,fill = stay_after_uni))+
  labs(title = "Prefer Study Buddies?",
       subtitle = "Comparing staying past uni schedule and study preference",
       caption = "Source: Survey results",
       x = "Number of UOA students",
       y = "Study preference (alone, with study buddies, both)")



Those UOA students that responded to my survey and don’t stay past uni schedule prefer studying alone.

Perhaps going home straight after and studying is more meaningful and productive for them?

Dynamic statements

average_campus_visits <- learning_data_renamed$uni_visit_per_week %>%
  mean()

paste("The average number of times the university students at UOA go to campus in a typical week is approximately", round(average_campus_visits, 1), "(using the mean)")
## [1] "The average number of times the university students at UOA go to campus in a typical week is approximately 3.5 (using the mean)"
min_study_hours_daily <- learning_data_renamed$study_hours_per_day %>%
  min()


paste("The minimum number of hours a university student at UOA spends studying per day is", min_study_hours_daily, "hours (using the min)")
## [1] "The minimum number of hours a university student at UOA spends studying per day is 2 hours (using the min)"

Creativity


My project demonstrates creativity as I have added css code to make the text, headings, preformatted text, and images more personalised (ie. similar to previous assignment). In addition, I have made sure to max width of images to 100% so it does not exceed the width. I have also used the function na.omit(learning_data_renamed) to remove any NA categorical values (ie. for analysing questions that were logically related). For instance, when analysing the reasons for staying post uni schedule, those who had answered no prior were not asked this question so their answers were set to NA values. That is one of the cases where I had to use this na.omit function.

Learning reflection


One important idea I learned from Module 2 Creating web-based dynamic reporting systems is how convinient R, google sheet and google form is to record data. This integration not only facilitates static analysis but also enables dynamic updates, eliminating the need to wait for additional results to start analysis. Thus, I did not have to wait for more results to add up and I was able to start my assignment early!

As mentioned in earlier sections, after finding out about the relationships, I was curious about experimenting and finding out such relationships were causal or not. Thus, I am more curious about finding out about various other tools to find out about more complex relationship and be more certain about the results. While the initial dataset only included 20 university students, I’m considering expanding the sample size ethically to conduct experiments and validate these relationships further.